Machine learning (ML) tasks are becoming ubiquitous in today's network
applications. Federated learning has emerged recently as a technique for
training ML models at the network edge by leveraging processing capabilities
across the nodes that collect the data. There are several challenges with
employing conventional federated learning in contemporary networks, due to the
significant heterogeneity in compute and communication capabilities that exist
across devices. To address this, we advocate a new learning paradigm called fog
learning which will intelligently distribute ML model training across the
continuum of nodes from edge devices to cloud servers. Fog learning enhances
federated learning along three major dimensions: network, heterogeneity, and
proximity. It considers a multi-layer hybrid learning framework consisting of
heterogeneous devices with various proximities. It accounts for the topology
structures of the local networks among the heterogeneous nodes at each network
layer, orchestrating them for collaborative/cooperative learning through
device-to-device (D2D) communications. This migrates from star network
topologies used for parameter transfers in federated learning to more
distributed topologies at scale. We discuss several open research directions to
realizing fog learning.

通过使用一种叫作 Fog learning 的新学习范式，从边缘设备到云服务器中智能地分布机器学习模型的训练，来增强联邦学习的三个重要维度：网络、异构性和邻近性，并考虑由各种接近程度异构设备组成的多层混合学习框架，通过设备到设备（D2D）通信进行协作学习，使其从联邦学习中用于参数传输的星型网络拓扑迁移到更分布式的规模。